TY - JOUR
T1 - Workflow for the Generation of Expert-Derived Training and Validation Data
T2 - A View to Global Scale Habitat Mapping
AU - Roelfsema, Chris M.
AU - Lyons, Mitchell
AU - Murray, Nicholas
AU - Kovacs, Eva M.
AU - Kennedy, Emma
AU - Markey, Kathryn
AU - Borrego-Acevedo, Rodney
AU - Ordoñez Alvarez, Alexandra
AU - Say, Chantel
AU - Tudman, Paul
AU - Roe, Meredith
AU - Wolff, Jeremy
AU - Traganos, Dimosthenis
AU - Asner, Gregory P.
AU - Bambic, Brianna
AU - Free, Brian
AU - Fox, Helen E.
AU - Lieb, Zoe
AU - Phinn, Stuart R.
N1 - Publisher Copyright:
© Copyright © 2021 Roelfsema, Lyons, Murray, Kovacs, Kennedy, Markey, Borrego-Acevedo, Ordoñez Alvarez, Say, Tudman, Roe, Wolff, Traganos, Asner, Bambic, Free, Fox, Lieb and Phinn.
PY - 2021/3/25
Y1 - 2021/3/25
N2 - Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.
AB - Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.
KW - Allen Coral Atlas
KW - calibration
KW - coral reefs
KW - habitat mapping
KW - training
KW - validation
UR - http://www.scopus.com/inward/record.url?scp=85103885391&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103885391&partnerID=8YFLogxK
U2 - 10.3389/fmars.2021.643381
DO - 10.3389/fmars.2021.643381
M3 - Article
AN - SCOPUS:85103885391
SN - 2296-7745
VL - 8
JO - Frontiers in Marine Science
JF - Frontiers in Marine Science
M1 - 643381
ER -